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概要と使いどころ
SPARC development workflow (Specification, Pseudocode, Architecture, Refinement, Completion). Use when: new features, complex implementations, architectural changes. Skip when: simple fixes, documentation, configuration.
日本語の概要は準備中です。原文の説明を表示しています。
Regulator-grade feature attribution for any LSTM/Transformer signal — single-entry PageRank ranks the top-K features that drove the prediction (ADR-126 Phase 6, ADR-123 single-entry PR)
日本語の概要は準備中です。原文の説明を表示しています。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
日本語の概要は準備中です。原文の説明を表示しています。
Use when reviewing slow initial loads caused by optional features. Prioritize code that is expensive to download or execute and not required for the first viewport, first input, or core task completion.
日本語の概要は準備中です。原文の説明を表示しています。
Use when reviewing security-sensitive web apps, SharedArrayBuffer usage, worker-heavy apps, editors, or measurement features that require cross-origin isolation. Check both headers and real browser behavior.
日本語の概要は準備中です。原文の説明を表示しています。
Use when the team wants to catch desktop app users up on recent features with a one-time "What's new" dialog (apps/examples/desktop-app). Guides deciding whether a catch-up is warranted, picking the highlights from the changelog, writing tight copy, adding the entry to webview/lib/whats-new-content.ts, and previewing it from Settings → About. Not for individual releases — those only appear on the About page's release notes.
日本語の概要は準備中です。原文の説明を表示しています。
Read, create, edit, and deploy web apps and projects in Lovable. Use to turn a product brief into a working website or prototype, inspect existing project code, update pages and features, and publish changes through Lovable's official MCP server.
日本語の概要は準備中です。原文の説明を表示しています。
Add end-to-end and API tests for features that already work, following the project's existing test harness. Use when the user asks for e2e tests, API tests, or automated coverage of an existing feature, or says "create qa automated tests for [feature]". Not for unit tests on work in flight, which the build writes.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.
日本語の概要は準備中です。原文の説明を表示しています。
Stateful QGIS CLI for projects, writable layers, features, layouts, exports, and qgis_process operations using the real QGIS runtime.
日本語の概要は準備中です。原文の説明を表示しています。
Predicts regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer activity, chromatin state, sequence-to-expression (log TPM), and de-novo gene annotation, plus a composite find-genes-then-predict-expression workflow. Use when the user has a gene symbol, a genomic region, or a DNA/FASTA sequence and wants any of these predictions, mentions Genomic Intelligence, genomicintelligence.ai, api.genomicintelligence.ai, or mcp.genomicintelligence.ai.
日本語の概要は準備中です。原文の説明を表示しています。
Implement Conversational Language Understanding (CLU) using the azure-ai-language-conversations Python SDK. Use when working with ConversationAnalysisClient to analyze conversation intent and entities, building NLP features, or integrating language understanding into applications.
日本語の概要は準備中です。原文の説明を表示しています。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
日本語の概要は準備中です。原文の説明を表示しています。
Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
日本語の概要は準備中です。原文の説明を表示しています。
Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.
日本語の概要は準備中です。原文の説明を表示しています。
Upgrade React applications to latest versions, migrate from class components to hooks, and adopt concurrent features. Use when modernizing React codebases, migrating to React Hooks, or upgrading to latest React versions.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when creating, managing, or working with Conductor tracks - the logical work units for features, bugs, and refactors. Applies to spec.md, plan.md, and track lifecycle operations.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when designing or reviewing a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
日本語の概要は準備中です。原文の説明を表示しています。
PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS).
日本語の概要は準備中です。原文の説明を表示しています。
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.
日本語の概要は準備中です。原文の説明を表示しています。
Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.
日本語の概要は準備中です。原文の説明を表示しています。
This workflow guides you through a systematic approach to identify missing features, prioritize them, and create detailed specifications for implementation.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
日本語の概要は準備中です。原文の説明を表示しています。
Create a new implementation plan file for new features, refactoring existing code or upgrading packages, design, architecture or infrastructure.
日本語の概要は準備中です。原文の説明を表示しています。